{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 共享单车数据探索 \n",
    "数据集来自Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）。 共731个数据点，涵盖了共享单车13种特征和非注册用户个数casual, 注册用户个数registered, 给定日期（天）总租车人数cnt，三个y标签。训练数据为2011年的数据，要求预测2012年每天的单车共享数量。原始数据集为csv格式, 使用pandas做数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path to where the data lies\n",
    "#dpath = './data/'\n",
    "data = pd.read_csv(\"day.csv\")  # return DataFrame\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数据各特征的分布，以及特征之间是否存在相关关系等冗余。\n",
    "\n",
    "我们可以借用可视化工具来直观感觉数据的分布。\n",
    "\n",
    "在Python中，有很多数据可视化途径。\n",
    "Matplotlib非常强大，也很复杂，不易于学习。 \n",
    "Seaborn是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图，而使用matplotlib就能制作具有更多特色的图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此处得到各属性的样本数目、均值、标准差、最小值、1/4分位数（25%）、中位数（50%）、3/4分位数（75%）、最大值\n",
    "可初步了解各特征的分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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FDk1ISlnGGDYerGR0cixZyXFuh9OvvnzOOI6daObvW0rcDkWpj2hCUsoqqW6grKaRuWPC9+qow6JxaUzPGs5DbxfQrlXAVYjQhKSUta6ggugID7MGYb9H3SUi3HTOeArK63htl1YBV6FBE5JSwImmVrYX1zA7N3nQN6QarKXTM8lKjuPBt/a7HYpSgCYkpQCnqndru2HRuDS3QxkwkREeblw8lg0Hq9hcWOV2OEppB32qb/lrF67DNQtyBzCS4LW1G9YVVDBhxDBGDI91O5wBddW8HH736h7uW7ufh6+b63Y4aojTKyQ15H1Ydpzjja1D6uqoQ0JMJF8+exyv7jzCxoOVboejhjhNSGrIe2//MVLio5icmeh2KK648eyxjEiM4X9e2oU2fqLcpAlJDWn5pTUcrKhn0bi0sOlmorvioyP55gWT2HSoin9+eMTtcNQQps+Q1JB23xv7iYn0MGcIvHvUmX+fm80j/yrg1y/v4vwpI4iM6Nvfqp09W4TQfb6oBpZeIakha9/RE7z4QRkLx6URFz00qnoHEhnh4fuXTGF/eR2rNha7HY4aojQhqSHrvrX7iI2M4KwJp3SrNSRdNHUkc8ek8L9rdnG0Vrs5VwNPE5Iakg5V1PHctlKuXZDLsBi9cw1O6w2/unwGdc1t3PH0B1rBQQ04TUhqSLpv7X4iPMJN54xzO5SQMmFEIj+4ZAqv7TrK3/TWnRpgmpDUkFNcVc/Tm4tZPi9nyL0IG4wbzsxj4bhUfvp8PkWV9W6Ho4YQTUhqyPntP/fgEeEr5453O5SQ5PEIv7lyJiLCN1du1T6T1IDRhKSGlE2HKnlmSwlfOnts2Pd51BvZKfHcefnpbC6s4tbHN9PS1u52SGoI0ISkhoy2dsOPV+eTOTyWWz8xwe1wQt6nTh/Fz5dN57VdR/nu37Zpv0mq32n1IjVg3H45ctXGInaUHOf3y2eRMERr1nV3G3xu4RhqGlr43zW7Ka1u4NKZo4n0+P8dqy+3qt4amkelGnJq6p2T6vy8VC6bOdrtcAaVW84bT11TK/e9sZ/S6kb+fW4OGYkxboelwpDeslNhzxjDT5/Pp7q+mZ9cNg0Zom3W9ZSI8P1LpnDN/Fwq65q5Z+1e3iuo6PVzpXZjaGhuo13fd1KWXiGpsLdiQxHPbCnhmxdMZOro4W6HM2hNz0oiJzWepzYV8fy2Ul758DCzc1KYnZvMiMRTq88bY6ioa6agvI4NByo5WttI+YkmymubqG9uo6nVSWiRHuGx9w4xLiOBi6aN5NLTR/d5W3pqcNCEpMLajpIafrw6n3MmZXDbJye6Hc6glxQXxRfPGkvBsTo2HKxk/cFK3iuoAOC+N/YxKimWxpZ26ppbqalvobap9aN5oyKEjGEx5KTGkxgTSWxUBDGRHmobW4mJimB7cQ0v7TjMb1/Zw03njOfKOdlDpjt55dCEpMJWTX0LX318E2kJ0fzuqll4PHqrri+ICOMzhjE+Yxj1Ta3sLT9BxYlmkuOjOFrbRGaUh4ToSBJjIxmTlsC4jAR2lBwnOT4qYBcf1yzIpb3d8Nquo9y7dh//9ewOntpYxB+vnzfAa6fcpAlJhaXq+ma+9JeNlFU3svIri0hNiHY7pLAUHxPJzOxkoPNadqXVXTfW6vEIF04dyQWnjeDlHYf55sqtXH7/u1wxJ0e33xChN2pV2CmtbuDKB95je3ENd189mzljUtwOSXWDiLBkxiie+PICqhtaeODN/ZRWN7gdlhoAmpBUWMkvreHy+9/lcE0jf/7iPJbOGOV2SKqH5oxJ5ambFxHhER5bd4iGZm3CKNxpQlJhobCinm+v3Mqn/+9ftLYbVn5lEWeO136OBrsJIxK5dkEutY0t/H1LsXaJEeb0GZJyhTGGAxV1HCiv41BFPUVV9fzouR10nG7Sh0WTnRJPdkocOSnx5KbGk5MaT1JcFNGRQqTHQ0l1A9uLa9haVMVrO4863UmcPY6bzx1Pij5zCBvZKfFcNDWTl/MPs+lQFXPzhnZ38+FME5IacAeP1fHSjjKKqhoQIDMplpk5yczLc571GAPltU2UVDewubCKF7aX0dZJO2pj0uL53MIxfPW88YzU7iTC0uKJ6ew5Wsvz20sZk5agLUWEKU1IasCcaGrl71tK2Fl2nOGxkXx2dhYzspI+etckUC2t1rZ2ymoaKaysp7axhZY2Q0tbO+nDYjg9O4nkeL0aCnceEa6ck8Pdr+3l6c3FfOWccdriRhjShKQGRGVdM3965wDHG1u4aOpIzhyfTnTkyY8wO2v485oFueSkxvd3mCqEJcVFcfG0TJ7dWsLuw7VMGaWtboQbrdSg+l1ZTQN/eHM/9c1t3HjWWM6bPOKUZKRUMOaMSSEtIZp/fnhE28ALQ3pWUP2qpLqBB98qwOMRbjpnHLlpCW6HpAaxCI9wwWkjOXy8kQ+Ka9wOR/UxTUiq39Q3tfL4+4eIjYrgK+eM0woHqk/MyE4ic3gsr+480mllFzX4BPUMSUQuAX4PRAAPG2N+5TM+BngUmANUAFcZYw7acXcANwJtwG3GmDWdlSkiXwO+CYwHMowxx+xwsdMvBeqB640xm3u85qpftRvDqk1F1Da2ctPZ43pd8cDtzv2Giq6+5/6atzs84jQx9Ni6Q2w+VEVEJ20U6n4xuHR5hSQiEcC9wBJgKnC1iEz1mexGoMoYMwG4C7jTzjsVWA5MAy4B7hORiC7KfAe4ADjks4wlwET77ybg/u6tqhpIr+86yp4jJ/j06aO0MoLqc1MyE8lNjWftnqP6LCmMBHPLbj6wzxhTYIxpBlYAy3ymWQb8xX5+CjjfXtEsA1YYY5qMMQeAfba8gGUaY7Z0XF35WcajxrEOSBYRbRcmBO07eoK1u44yOyeZ+foSo+oHIsLiCelU17ewq6zW7XBUHwkmIWUBRV5/F9thfqcxxrQCNUBaJ/MGU2ZP4kBEbhKRjSKysby8vIsiVV9raWvnua0lpCZEs2xWlr4rovrNaaOGkxQXxboDFW6HovpIMM+Q/J1RfK+RA00TaLi/RNjVdXcwcWCMeRB4EGDu3Ll6LT/A3t5bTkVdMzecmTegVbu7eodJhZ8IjzAvL5VXdx6hvLZJW28IA8GcMYqBHK+/s4HSQNOISCSQBFR2Mm8wZfYkDuWiosp63thdzvSsJCaOTHQ7HDUEzMtLIUJEr5LCRDAJaQMwUUTGikg0TiWF1T7TrAaus5+vAF43TrO8q4HlIhIjImNxKiSsD7JMX6uBL4hjIVBjjCkLIn41QH76fD4eET6lXT6oAZIYG8X0rOFsPlRFU6t2TzHYdZmQ7DOhrwFrgJ3AKmNMvoj8TEQus5M9AqSJyD7g28Dtdt58YBXwIfAycKsxpi1QmQAicpuIFONcAW0XkYftMl4ECnAqRjwE3NLrtVd95vVdR3h151HOP20ESXFRboejhpBF49Joam1na1G126GoXpJw7l9k7ty5ZuPGjW6HEfba2w1L736bptZ2vnjW2E7fC1HKn86e83X1fpMxhnvX7qPNGG775MSTKtLo88OeEZFNxpi5A71cbalB9drL+YfZdbiWb5w/UZORGnAiwty8VI4cb6KsptHtcFQvaEJSvdLebvjdq3sYn5HApTNHux2OGqJOz0oiQkRv2w1ympBUr/zjgzL2HDnBNy+YpFdHyjXxMZFMzkxkW1G1tm83iGlCUj3WZq+OJo0cpjXrlOtm5SRT29RKQfkJt0NRPaQd9Kkee2F7KfvL67j/2jPw6NWR6oW+aJh1cmYisVEethRV63twg5ReIakeMcZw/xv7mTRyGBdPy3Q7HKWIivAwIyuJ/NIafSdpkNKEpHrknX0V7Dpcy5fOHqdXRypkzMpJoaXN8GHpcbdDUT2gCUn1yENvF5CRGMOyWVqzToWOMWnxpMRHaW27QUqfIalTdNVQ6Z4jtby5p5zvXjSJmMiIAYxMqc55RJiZncybe8o50dTqdjiqm/QKSXXbw28XEBvl4doFY9wORalTzMhOwgD5pTVuh6K6SROS6pajtY08u6WUK+fkkJLQu27JleoPmcNjSUuIZkeJJqTBRhOS6pa/vneIlvZ2blw81u1QlPJLRJiRlURBeR0VJ5rcDkd1gyYkFbTW9naeWF/I+VNGkJee4HY4SgU0Pcu5bbcm/4jboahu0ISkgpZfcpxjJ5r5/KI8t0NRqlOjkpzbdi/t0C7TBhNNSCpo6w5UkJcWz9kT0t0ORalOiQjTs5J4d38FlXXNboejgqQJSQWlrKaBQxX1fG7hGH0RVg0K07OSaGs3/DP/sNuhqCBpQlJBWVdQSaRHuGJOttuhKBWU0Umx5KbG848P9LbdYKEJSXWpsaWNrUVVzMxOJjleq3qrwUFEWDpjFO/ur6C6Xm/bDQaakFSXNhdW0dJmWDguze1QlOqWpTMyaWs3vPKh1rYbDDQhqU4ZY3j/QCXZKXFkpcS5HY5S3TIjK4nRSbGs0edIg4K2Zac6dbCinvLaJv5tdhbQN/3WKDVQRISLp2fy+PuFnGhqZViMnvJCmV4hqU5tOFhJTKSH07OT3Q5FqR5ZMn0Uza3trN111O1QVBc0IamA6pta2VFSw6ycZKIjdVdRg9OcMSmkD4vmZb1tF/L0LKMC2lxUTWu7Yf7YVLdDUarHIjzCRdMyWbvrKI0t2pNsKNOEpPwyxrDhQCU5KXGMStLKDGpwu2RaJvXNbby995jboahOaEJSfh2sqKf8RJNeHamwsHBcGsNjI3l5h962C2WakJRfGw5WEhvlYUaWVmZQg190pIcLpo7k1Z1HaGlrdzscFYAmJHUKrcygwtGS6aOoaWhhXUGF26GoAPRso07xUWWGPG2ZQYWPsyemkxAdwYvatl3I0oSkTuJdmSEzKdbtcJTqM7FREZx/2kjW5B+hVW/bhSRNSOok6w9U2soMenWkws/SGZlU1jWz/kCl26EoPzQhqZM8ub7QVmZIcjsUpfrcuZNGEBcVwYvak2xI0oSkPlJV18yLOw5rZQYVtuKiI/jklBG8vOMIbe3G7XCUDz3rqI88vbmY5tZ2rcygwtqSGZkcO9HExoN62y7UaEJSgFOZ4cn1hczKSdbKDCqsfWLyCGIiPbykL8mGnKASkohcIiK7RWSfiNzuZ3yMiKy0498XkTyvcXfY4btF5OKuyhSRsbaMvbbMaDv8ehEpF5Gt9t+XerPi6mTvFVSwv7yOaxfkuh2KUv0qISaST0wewUs7ymjX23YhpcuEJCIRwL3AEmAqcLWITPWZ7EagyhgzAbgLuNPOOxVYDkwDLgHuE5GILsq8E7jLGDMRqLJld1hpjJll/z3cozVWfj2+rpCkuCgunTna7VCU6ndLZmRy5HgTGw9VuR2K8hLMFdJ8YJ8xpsAY0wysAJb5TLMM+Iv9/BRwvoiIHb7CGNNkjDkA7LPl+S3TzvNJWwa2zM/0fPVUMI4eb2RN/mGunJNNbFSE2+Eo1e8uOG0ksVEeVm8rcTsU5SWYhJQFFHn9XWyH+Z3GGNMK1ABpncwbaHgaUG3L8Lesy0Vku4g8JSI5QcSugrBiQxGt7YZrF45xOxSlBkRCTCQXTs3kxQ8Oa9t2ISSYhCR+hvneeA00TV8NB3geyDPGnA68ysdXZCcHInKTiGwUkY3l5eX+JlFeWtvaeXJ9IYsnpDM2PcHtcJQaMMtmjqayrpl/7dMuKUJFMAmpGPC+GskGSgNNIyKRQBJQ2cm8gYYfA5JtGSctyxhTYYxpssMfAub4C9YY86AxZq4xZm5GRkYQqze0vb7rKGU1jXxOr47UEHPOpAyS4qJYvdX3dKbcEkxC2gBMtLXfonEqKaz2mWY1cJ39fAXwujHG2OHLbS28scBEYH2gMu08a20Z2DKfAxCRUV7LuwzY2b1VVf789f1CMofHcsFpI9wORakBFR3pYemMTNbkH6ahWXuSDQVdJiT7POdrwBqcJLDKGJMvIj8TkcvsZI8AaSKyD/g2cLudNx9YBXwIvAzcaoxpC1SmLesHwLdtWWm2bIDbRCRfRLYBtwHX927V1f7yE7y1p5yr5+cSGaGvpKmh57KZWdQ3t/HqziNuh6KAyK4nAWPMi8CLPsN+5PW5EbgywLy/AH4RTJl2eAFOLTzf4XcAdwS54xCyAAAbr0lEQVQTrwrOn985SHSEh2v03SM1RM0fm0rm8Fie21qqrzyEAP1ZPETV1Lfw1KZiLps1mozEGLfDUcoVER7h0pmjeHPPUarrm90OZ8jThDRErdxYSENLGzecled2KEq5atmsLFraDM9v08oNbgvqlp0KL4+9d4j73tjP2PQEthXVsK2oxu2QlHLN9Kwkpo0ezooNRXx+UZ7b4QxpeoU0BO0sO051fQtnjtdWvZUCWD4vh/zS4+wo0R9nbtKENAS9u/8YKfFRnDZquNuhKBUSLpuVRUykhxUbCt0OZUjThDTEbC6s4mBFPYvGp+MRfw1jKDX0JMVF8akZo3huS6m+k+QifYY0xNy3dj9xURHMy0txOxSl+t0T73d+xeP9ysNV83J4ZksJ//igjCvmZPd3aMoPvUIaQnYfruXVnUc4c3waMZHaqrdS3uaPTWVsegIr9badazQhDSH3v7GP+OgIFmllBqVOISJcNS+HDQer2Hf0hNvhDEmakIaIwop6Vm8r5doFucRH651apfy5/IxsoiM8/OXdg26HMiTpmWmI+MNb+4n0ePjS2eN4bedRt8NRKiT4e8Y0IzuJFRsK+daFk0hNiHYhqqFLr5CGgLKaBv62qZjL52Qzcnis2+EoFdIWT0inpc3w13WH3A5lyNGENAT8/tW9YOCW88a7HYpSIW/k8Fgmj0zk0fcO0tiiVcAHkiakMLfv6AlWbSzi2oW55KTGux2OUoPC4onpHDvRzLNbStwOZUjRhBTmfvvKbuKiIrj1ExPcDkWpQWNcegLTRg/nobcLaG83boczZGhCCmPbiqp58YPDfOnscaQP0y4mlAqWiHDTOePYX17Ha7u0EtBA0YQUxv53zW5SE6L58jnj3A5FqUFn6YxRjEmL5//9czdtepU0IDQhhanXdh7hX/uOcesnJjAsRmv3K9VdUREevnvRZHYdruW5rfosaSBoQgpDDc1t/Oi5fCaOGMbnF45xOxylBq1PzRjFjKwk/t8/92iNuwGgCSkM3f36XkqqG/jvz0wnOlI3sVI95fEIty+ZQkl1g76XNAD0bBVm9h6p5aG3CrhiTjYLxmmbdUr11lkT0jl7Yjr3rN3H8cYWt8MJa5qQwogxhh8+u4OEmEjuWDLF7XCUChu3L5lCdX0Lv3tlr9uhhDVNSGHksXWHeP9AJbcvmUKaVvNWqs9MG53E5xbm8qd3D7DpUKXb4YQtTUhh4sPS4/z3P3byickZLJ+X43Y4SoWd25ecxuikOL73t+1awaGfaEIKA/XNrXz9yc0kx0XxmytnIto1uVJ9blhMJHdefjoFx+q465U9bocTljQhhYGfrv7QOUiumqW36pTqR4snpnP1/FweeruAzYVVbocTdjQhDXIr1heycmMRXz13PGdNSHc7HKXC3n8sncKopDhufXwzR483uh1OWNFX+Aex13Ye4T+f3cE5kzL41oWTThrnr+MxpVTvJcZG8eAX5nDF/e/x5cc2sfKmhcRGRbgdVljQK6RBanNhFbc+sZmpo4Zz/7VnEBWhm1KpgTJtdBK/Wz6LbUXVfP+p7Rijbd31BT2LDUJ7jtRy4583MHJ4LH+8fh4J2ladUgPu4mmZfP+SyazeVsqv1+zWpNQH9Ew2yKwrqOCmRzcSExXBX26YT0aiVmJQyi1fPXc8RZUN3P/GfuqbWvnxpdPweLSWa09pQhpEnt9WyndWbSM3LZ4/3zCP7BTtAVap/tLVc9hrFuQiIvzys9NJjI3kwbcKqGlo4X+vnKm30HtIE9Ig0NTaxt2v7eXetfuZn5fKg1+YQ3J8tNthKaVwOvO7Y8kUkuOj+PXLuzlyvInfXjWTUUlxboc26GgaD3H5pTUsu+cd7l27n3+fm82jN87XZKRUiBERbjlvAr+5cibbiqu5+K63eH5bqdthDTp6hRSijp1o4g9v7udP7xwkJSGaR66by/mnjXQ7LKVUJ66Yk83cMSl8c+VWvv7kFl78oIzvXDSZCSOGuR3aoKAJKcQcOd7IH/91gEffO0RTaxv/dkY2/7n0NFIS9KpIqcEgLz2Bp25exANv7ue+N/azJv8wn5mVxdc+OYFxGZqYOhNUQhKRS4DfAxHAw8aYX/mMjwEeBeYAFcBVxpiDdtwdwI1AG3CbMWZNZ2WKyFhgBZAKbAY+b4xp7mwZg92JplZe+fAwz2wu4Z19xwBYNiuLr3eyA+uLr0qFrsgID1/75ESunp/LH94q4C/vHuSZLSXMy0vh387IZumMUSTFRbkdZsiRrurOi0gEsAe4ECgGNgBXG2M+9JrmFuB0Y8zNIrIc+Kwx5ioRmQo8CcwHRgOvAh1NCvgtU0RWAc8YY1aIyAPANmPM/YGW0Vnsc+fONRs3buzeN9LP2tsNJdUN7D5cy6bCKt7bX8EHJTW0tRuyU+L47OwsLj8jm7z0hE7L0YSklLuuWZAb9LRHaxt5alMxT28qZn95HREeYXpWEovGpbFgbCpTRiWSOTw2ZBpGFpFNxpi5A73cYK6Q5gP7jDEFACKyAlgGfOg1zTLgJ/bzU8A94nyzy4AVxpgm4ICI7LPl4a9MEdkJfBK4xk7zF1vu/YGWYQb4bbT2dkObMbS1G1rbDc2t7R/9a2hpo665lYbmNmoaWqiqb6a6voWjxxsprWmkrKaBA+V11DU7TddHeoSZOcl89dzxnDs5gzm5KfoOg1JhaERiLLecN4Gvnjue7cU1vPLhEd4rqODhtwt44M39ACTGRjI+YxhZyXGMSoolMymWpLgokuOjSYqLIj46grjoCOKjI4iO8BAV6SHK4yHCI0R4BI8QMgmtp4JJSFlAkdffxcCCQNMYY1pFpAZIs8PX+cybZT/7KzMNqDbGtPqZPtAyjgWxDt3y0gdlfGPlVjBgMBgD7cbQ3sPUlxgbyeikOEYlxzJ3TCqTMxOZNDKRKZmJ2sqCUkOIiPMjdGZOMgB1Ta18UFLD3iO17D5SS0F5HR+WHee1XUdobGnvdvkeAY8IHhFuOmcc3714cl+vQr8K5mzoL+X6npoDTRNouL/q5p1NH2wciMhNwE32zxMistt+TqcfklewdvR9ka6uTz8Ip/UJp3WB8FqfPluXa/uikN4LuD7f+yV8r+fljun5rD0XTEIqBry7IM0GfCvYd0xTLCKRQBJQ2cW8/oYfA5JFJNJeJXlPH2gZJzHGPAg86DtcRDa6cU+0v+j6hK5wWhcIr/UJp3WB8FufYF6M3QBMFJGxIhINLAdW+0yzGrjOfr4CeN0+21kNLBeRGFt7biKwPlCZdp61tgxsmc91sQyllFJhoMsrJPu85mvAGpwq2n80xuSLyM+AjcaY1cAjwGO20kIlToLBTrcKpwJEK3CrMaYNwF+ZdpE/AFaIyH8DW2zZBFqGUkqp8NBlte9wISI32dt5YUHXJ3SF07pAeK1POK0LhOH6DJWEpJRSKrRp46pKKaVCQtgkJBH5XxHZJSLbReTvIpLsNe4OEdknIrtF5GKv4ZfYYftE5Hav4WNF5H0R2SsiK23Fi5AQKOZQIyI5IrJWRHaKSL6IfMMOTxWRV+x3+4qIpNjhIiJ32/XaLiJneJV1nZ1+r4hcF2iZA7BOESKyRUResH/73U9sJZ6Vdl3eF5E8rzL87osDTUSSReQpe8zsFJFFg3XbiMi37D62Q0SeFJHYwbRtROSPInJURHZ4DeuzbSEic0TkAzvP3SIh/PasMSYs/gEXAZH2853AnfbzVGAbEAOMBfbjVKSIsJ/HAdF2mql2nlXAcvv5AeCrbq+fjSVgzKH2DxgFnGE/J+I0FTUV+DVwux1+u9d2Wgq8hPO+2ULgfTs8FSiw/6fYzykurdO3gSeAFzrbT4BbgAfs5+XAys72RZfW5S/Al+znaCB5MG4bnBfmDwBxXtvk+sG0bYBzgDOAHV7D+mxb4NRsXmTneQlY4sY+F9R34XYA/bSBPws8bj/fAdzhNW6N3TiLgDVew++w/wTnfaiO5HbSdC6vl9+Y3Y4ryNifw2m7cDcwyg4bBey2n/+A055hx/S77firgT94DT9pugGMPxt4Dadpqxc620869jH7OdJOJ4H2RRfWZbg9iYvP8EG3bfi4BZdU+12/AFw82LYNkMfJCalPtoUdt8tr+EnThdq/sLll5+OLOL8EwH/TR1mdDO+s+SK3BYo5pNnbIrOB94GRxpgyAPv/CDtZd7fTQPsd8H2goz2XoJu5Aryb0gqFdRkHlAN/srcgHxaRBAbhtjHGlAC/AQqBMpzvehODd9t06KttkWU/+w4PSYMqIYnIq/Y+se+/ZV7T/CfOO0+PdwzyU1RnzRQF1USRS0I5Nr9EZBjwNPBNY8zxzib1MywktoeIfBo4aozZ5D3Yz6RdNXPl+rpYkTi3iO43xswG6nBuCwUSsutjn60sw7nNNhpIAJZ0ElfIrkuQwul8dopB1bKnMeaCzsbbB3mfBs439vqUvm2+yG3BNOMUMkQkCicZPW6MecYOPiIio4wxZSIyCjhqhwdat2LgPJ/hb/Rn3H6cBVwmIkuBWJxbXr+j+81chcr2KwaKjTHv27+fwklIg3HbXAAcMMaUA4jIM8CZDN5t06GvtkWx/ew7fUgaVFdInRGnw78fAJcZY+q9RvVl80VuC6YZp5Bga/I8Auw0xvzWa5R3E1C+TUN9wdYiWgjU2FsVa4CLRCTF/hq+yA4bMMaYO4wx2caYPJzv/HVjzLV0v5mrQPvigDLGHAaKRKSjKejzcVpTGXTbBudW3UIRibf7XMe6DMpt46VPtoUdVysiC+338wVC53x2KrcfYvXVP2Afzj3UrfbfA17j/hOn1sxuvGqY4NRY2WPH/afX8HE4O+M+4G9AjNvr11XMofYPWIxza2C71zZZinO//jVgr/0/1U4vwL12vT4A5nqV9UW7LfYBN7i8XufxcS07v/sJzlXU3+zw9cC4rvZFF9ZjFrDRbp9ncWpmDcptA/wU2IXTqP5jODXlBs22wenEtAxowbmiubEvtwUw1343+4F78KnMEkr/tKUGpZRSISFsbtkppZQa3DQhKaWUCgmakJRSSoUETUhKKaVCgiYkpZRSIUETUg+JyFm2Vd0TIvIZF5b/Z3F61Q05IpInIsa+eOhv/EER8fuSs4icLSK7g5m2j2I9aXm9KOcNEflSD+f9iYj81X7OtftURG9j8rOcPt1n7Dae0FflDaSBil1ErheRf/X3cuyy+m3f6a1g970hm5D64ET3M+AeY8wwY8yzvS2/v0+8vTGQB5Ux5m1jzOSupxycy+uKMabQ7lNtbsfirTcJVw2MYPedgTyeu2vIJqQ+MAbIdzuIYAS6UlFKhY7Bcpz2Z5whkZDE6cztGREpF5EKEbnHDveIyA9F5JA4HVg9KiJJdtx5IlLsU85HVxn2NsgqO0+tOB14zbXjHgNygeftJe73A8T1ZXE6taoUkdUiMtoO7+iTqGP+GJ/5/JYvIpfZOKrtL87Tupj+byJyWERqROQtEZkW5Pd5vYi8IyJ3iUgl8BM7/IvidMZWJSJrRGSM1zxGRG62tyGrRORe2zzJaTj9ySyysVXb6T8lTkvRx0WkSER+EkxsXuaJyId2WX8SkVhb7inb1SvGKSJyQESW279Hi8jTdr85ICK3eU07X0Q22viOiMhvA5R50vLsPvRdcTo/qxGnM7dYr/HLRGSrLXe/OE1W+Zb50S04+/dJtzDFafrpTbtfvgKkdzLtGyLyc7s9a0XknyLiPf0X7PFRISL/JV1faaeL0+FbrY3Bex84U0Q22PXeICJn2uG/AM4G7rH7wD1e5V3gu88E+J4jROQ/7HdWKyKbRCTHjvu93YeO2+Fne83ndzv620/k5ON/voi8J86xViYi90iQHW2KyA32OKkVkQIR+YrXuPNEpFhEviPOOalMRG7wGp8mzrniuIisB8Z3spyObX2jiBQCr9vhC0XkXRv7NhE5z2ueseKcC2rFaWz6Xvn4dq/vvnO9jb9WnOPjWgl8PMeIyG9EpNB+zw+ISJzPOv9ARA4Df7LDPy3OsVBt4z3dK87ZIrLZLnslTgsZXXO7qQicTue2AXfhtNQbCyz2aQpjHDAMeAZ4zI47D6eBSO+yDgIX2M8/ARpxmquJAP4HWOdv2gBxfRKnodUzcJoi+T/grW7Mf9J4YBJOq8oXAlE4XRnsA6IDlWfXP9Eu/3fAVq9xfwb+O8Cyr8dp8fzrOA3oxgGfscs7zQ77IfCu1zwGpy+ZZJzkWA5c4lXev3yWcR4wA+dHzenAEeAzdlyeLS+yk+9mB04jkanAOx3r4rtdO74Xux0KgU/b4R6cbgZ+hNPB3DicTskutuPfAz5vPw8DFgaIxd/y1uO0HJ0K7ARutuPm43RXcKFdfhYwxY57g487vPsJ8FevMk/6Pmxsv7Xb9RygtmN6P9O+gdPkyyS7Hd8AfmXHTQVO4DTTFI3TDUMLAfZLnH2m1i4zBvh9x3a161oFfB5n/7ja/p3mu37B7DN+lv09nKZuJuM0fzPTq+zP4TSVEwl8BzgMxHa2HX23m5/jfw5OB3aR9jvdidPivHfsEwLE+imcRCLAuUA9H3c2eR7OsfUznON4qR3f0RneCpzOAROA6UAJPseOn/3iUTt9HM4+VWHL9eDsaxVAhtf38Ru7vRcDx/Gz79jyjgOT7bhRwLROjuff4bSTl4pzznke+B+fdb4TZ7+JwzkejwILcM6v19nvP8bGdgj4lv2OrsDZL/2er06Ko6sJ+vsfTudZ5fg5eeG04XSL19+T7YpFBrFD/gR41WvcVKDB37QB4noE+LXX38PssvOCnP+k8cB/Aau8/vbYnfW8IMtLtjtbktfJpbOEVOgz7CXgRp/l1wNjvA7QxV7jV/Fxj5Wn7MB+lvk74C7fA6OT7+Zmr7+XAvu9dn7fBPFTnDa+PuE1fIGfdbwD+JP9/JadL72LuP0t73Nef/+aj3sY/UPHOvop5w2CSEg4J+5WIMFr/BN0npB+6DXtLcDL9vOPgCe9xsUDzYH2I7vPrPDZp9twfhh8HljvM/17wPW+6+c1PuA+42fZu4FlnW0Lr2mrgJmdbUff7dbVMQR8E/i7T+x+E5KfeZ8FvuG13Aa89m2cE/NCnBNzC/ZHih33S7pOSN7t6f0A+6Pba9ganBN+x74T7zXur/72HZyEVA1cju1N12ue671jwkm8dcB4r2GLcFpR71jnZuyPBDvsfuDnfrbxuTg/eErxajMPeJcgElIo3LLLAQ6ZjzvT8jYaJ9N2OITzZY8MsuzDXp/rgVgJ/v7nScs2xpzA+aXS086tfMtrx2kM1m959hbHr+wtjuM4Bxt43d7pQpHP32OA39vL62qcJvfFZ/m+39ewQIWLyAIRWSvO7bIa4OZuxOYb3yGc7yeQm3Gu5tZ6DRsDjO5YH7tO/8HH+8aNOFcVu+ztp093I7ZA30MOztVKb4wGqowxdV7DDgWauIt4RuP1PRqnlfuKLsrynv4Ezn4wmlOPtY64utrfg91nAn539vbXTnursBqnS4iOfalH21FEJonIC+Lc8j6OkxiC2j9FZImIrBPnVn01zg8m73krfM5XHeudgXN+8t23u+I9/RjgSp/9ejHOFc5ooNKc3JuB73EOgN2/rsI5dspE5B8iMiXA8jNwfsxs8lrmy3Z4h3JjTKNPnN/xiTOHj/elEmMzkRXM9xASCakIyA2QKEpxVrxDxy+EIzgZPb5jhDhVHTMInuli/EnLFqdHzTScq5qelO9bnuBswJIA01+D0/HYBTgHaF7HrD1cfhHwFWNMste/OGPMuz0oC5xf9auBHGNMEs596WBjg5P7dMml8z5absbZR+7yGlaE8wvOe30SjTFLAYwxe40xV+P0tHkn8JTdhr1RRCfPBLyctG8CmV6fy4AUn1hyexhPGV593dh7/mldzPPR9y5O54mpON+977HWEVeg/bO7/H539nnRD4B/x7ntlYxzW1Sg0+3Y1fF/P04L4BONMcNxfqx0uX+K8zz4aZzbYiNtPC8GMy/OnZ5WTt23u+L93RbhXCF579cJxphf4WzvVBHx3rdyCMAYs8YYcyG2G3PgIT/LA+fRRAPOLb2OZSYZY7x/XPg7n/zCJ854Y0xHy+VZ9hzXIah9PBQS0nqcFfiViCSISKyInGXHPQl8yz7IG4bzK2el/XWyB+eK51PidAT3Q5z7l8E6gvPcIZAngBtEZJbdSX8JvG+MOdjD8lcBnxKR82283wGacC5l/U2faMdX4Bx4vwxyuYE8ANwhtmKEiCSJyJVBznsEyJaTHwon4vxaaxSR+TgJtDtuFZFsEUnFOVms7GTaWuAS4BwR+ZUdth44bh+0xtkryukiMg9ARD4nIhn2SrTaztPbqtSP4OwT54tT4SYrwK/OrTbWXHEq4dzRMcIYcwin24efiki0iCwGLu1hPE8Bl4pTGSEa59ZWVyfOpSKy2E7/c5x9ugjnpDtJRK4RkUgRuQrnNvcLdr6ujpeuPAz8XEQmiuN0EUnD2Y9asbftReRHOB0gAp1ux66O/0ScZygn7Db6apBxRttyyoFWEVmC07dQl4xT3foZ4Cfi9M80lY/7NArWX3G26cV2n44Vp1JBtte+8xO77ywiwL4jIiPFqUSVgHMeOcHH+/9Jx7P9bh8C7hKREXb+LBG5uJM4HwJutndKxJ67PyUiiTi3eluB2+y+9G84z1+75HpCshvxUmACzkPrYpxLTYA/4vRv8hZwAKeSwtftfDU499MfxvkVV8fJfcd35X+AH9rLze/6ies1nOc+T+MkzPE4nbP1qHxjzG6ch7f/h/OL5FLgUmNMc4B4HsW5zC3B6XBsXTeWfQpjzN9xfmGusLcwduC/q2d/Xsep4n5YRI7ZYbcAPxORWpxnGau6GdITwD9xKiIUAJ2+NGeMqcZ5wLtERH7utd/Mwtk3juHsC0l2lkuAfBE5gfPwfrnPLYduM8asB27AqYBTA7zJqVcVGGNewUmw23EqXrzgM8k1OM/AKoEf42zrnsSTj3M8rMDZR2txnmc0dTLbE3aZlTgP/q+1ZVXg9Lb8HZwfQd/HqUDSsb1/D1whTm26u3sQ7m9x9pF/4iSKR3Aejq/Beb65B2d/b+Tk21B+t2MQx/93cb7nWpyTZ2c/eD5ijKkFbrOxVtkyutMJ5tdwbt8dxnlm96duzIv9cbAM50daOc538T0+Pldfi/N8pwLnmFmJ/+3twdmWpTjb+lyc7wv8H88/wKn0tM6eH17FeWYfKM6NwJdx+leqsvNeb8c1A/9m/67COZ8/468cX9ofklJhwt5FqMa5TXXA7XhU/xOnSvUuY8yP3Y6lL7h+haSU6jkRudTeHkrAee7xAR9XgFFhRkTmich4e8v4EpyrqVNaihmsNCEpNbgt4+NKCRNxbmnpbY/wlYlTBf8EcDfwVWPMFlcj6kN6y04ppVRI0CskpZRSIUETklJKqZCgCUkppVRI0ISklFIqJGhCUkopFRI0ISmllAoJ/x/ebANmMaz5NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（给定日期（天）总租车人数cnt）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('count of total rental bikes including both casual and registered', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data.shape[0]), data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，数据大多集中在均值（4504）附近，和正态分布比较接近。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输入属性的直方图／分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.season, order=[1, 2, 3, 4]);\n",
    "plt.xlabel('Season(1:springer, 2:summer, 3:fall, 4:winter)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.yr, order=[0, 1]);\n",
    "plt.xlabel('year (0: 2011, 1:2012)')\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.mnth, order=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);\n",
    "plt.xlabel('Month ( 1 to 12)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.holiday, order=[0, 1]);\n",
    "plt.xlabel('holiday (0: no, 1:yes)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.weekday, order=[0, 1, 2, 3, 4, 5, 6]);\n",
    "plt.xlabel('weekday (day of the week)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.workingday, order=[0, 1]);\n",
    "plt.xlabel('workingday (1=workingday 0=weekend)(if day is neither weekend nor holiday is 1, otherwise is 0.)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.weathersit, order=[1, 2, 3, 4]);\n",
    "plt.xlabel('weather condition (1:sunny 2:foggy 3:light snow/light rain 4:heavy snow/heavy rain)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized temperature in Celsius. The values are divided to 41 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.atemp.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized feeling temperature in Celsius. The values are divided to 50 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.hum.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized humidity. The values are divided to 100 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.windspeed.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized wind speed. The values are divided to 67 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "X = data.drop(columns=['instant', 'dteday', 'yr', 'casual', 'registered', 'cnt']) \n",
    "# X_train = X[X[\"yr\"] == 0].drop(columns=['yr'])  # training set = select rows containing '2011'  (365, 11)\n",
    "# data = X_train\n",
    "cols=X.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = X.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('BikeShare_correlation.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp and atemp = 0.99\n",
      "season and mnth = 0.83\n",
      "weathersit and hum = 0.59\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of only the highly correlated pairs\n",
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(data, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}